library(tidyverse)
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## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
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library(dplyr)
library(ggplot2)
library(readr)
gun_violence <- read_delim("/Users/zzy/Desktop/firearm mortality in each state in 2021 .csv")
## Rows: 50 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): State, abbr
## dbl (3): Year, Death_Rate, Deaths
##
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gender <- read_delim("/Users/zzy/Desktop/gender ratio in each state in 2021.csv")
## Rows: 50 Columns: 5
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## Delimiter: ","
## chr (2): State, abbr
## dbl (3): Male_Ratio, Female_Ratio, Total_Ratio
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wealth <- read_delim("/Users/zzy/Desktop/us-real-per-capita-gdp-2022-by-state.csv")
## Rows: 50 Columns: 3
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## Delimiter: ","
## chr (2): State, abbr
## num (1): Real_GDP_per_Capita
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weapon <- read_delim("/Users/zzy/Desktop/number-of-registered-weapons-us-2021-by-state.csv")
## Rows: 50 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): State, abr
## num (1): Number_of_ Registered_Weapon
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
1.The distribution of gun violence in each state in US
library(usmap)
state_geometries <- statepop %>%
filter(full!="District of Columbia")
violence_geometries <- merge(state_geometries, gun_violence, by.x = "full", by.y = "State", all.x = TRUE)
write_csv(violence_geometries, "violence_geometries.csv")
p <- plot_usmap(data = violence_geometries, values = "Death_Rate", color = "red") +
scale_fill_continuous(name = "Death Rate", label = scales::comma) +
theme(legend.position = "right")+
ggtitle("The Distribution of Gun Violence in Each State")
p
2.The relationship between gender and gun violence
# merged data
gender_violence <- gun_violence %>%
left_join(gender,by="State")
write_csv(gender_violence, "gender_violence.csv")
gender_violence1 <- gender_violence %>%
pivot_longer(cols=c(Male_Ratio,Female_Ratio),
names_to="gender",
values_to="ratio")
gender_violence1 %>%
ggplot( aes(x = ratio, y =Death_Rate,color=gender)) +
geom_point()+
labs(title = "Relationship between Death Rates and Gender Ratio",
x = "Gender Ratio",
y = "Death Rate")
library(plotly)
##
## Attaching package: 'plotly'
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## last_plot
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## filter
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## layout
gender_violence %>%
plot_ly(x = ~Death_Rate,
y = ~Male_Ratio,
z = ~Female_Ratio,
text = ~State,
type = "scatter3d",
mode = "markers",
marker = list(size = 5)) %>%
layout(
scene = list(
xaxis = list(title = "Death Rate"),
yaxis = list(title = "Male Ratio"),
zaxis = list(title = "Female Ratio")
),
title = "Relationship between Death Rates, Male Ratios, and Female Ratios"
)
3.The relationship between wealth and gun violence
# merged data
wealth_violence <- gun_violence %>%
left_join(wealth,by="State")
write_csv(wealth_violence, "wealth_violence.csv")
wealth_violence %>%
ggplot(aes(x = reorder(State, Real_GDP_per_Capita), y = Death_Rate, color = reorder(State, Real_GDP_per_Capita))) +
geom_line(aes(group = 1), alpha = 0.7) +
geom_point(aes(group = 1), size = 3, alpha = 0.7) +
geom_smooth(method = "lm", se = FALSE, aes(group = 1), color = "yellow3", alpha = 0.5) + # Adjust alpha here
labs(title = "Real GDP per Capita by State",
x = "Real GDP per Capita for states in increasing order",
y = "Death Rate") +
theme_minimal() +
theme(panel.grid = element_blank(),
axis.text.x = element_blank(),
axis.line = element_line(color = "#95a5a6"),
text = element_text(family = "Arial", size = 10))
## `geom_smooth()` using formula = 'y ~ x'
wealth_violence %>%
ggplot( aes(x = reorder(State, Real_GDP_per_Capita), y = Death_Rate, fill = reorder(State, Real_GDP_per_Capita))) +
geom_bar(stat = "identity", color = "#2c3e50", alpha = 0.7) +
geom_smooth(method = "le")+
labs(title = "Real GDP per Capita and the death rate by State in 2021",
x = "Real_GDP_per_Capita for states in increasing order",
y = "Death Rate") +
theme_minimal() +
theme(panel.grid = element_blank(),
axis.line = element_line(color = "#95a5a6"),
text = element_text(family = "Arial", size = 10),
axis.text.x = element_blank())
## `geom_smooth()` using formula = 'y ~ x'
4.The relationship between number of firearms and gun violence
# merged data
weapon_violence <- gun_violence %>%
left_join(weapon,by="State")
write_csv(weapon_violence, "weapon_violence.csv")
weapon_violence %>%
ggplot( aes(x = reorder(State, Deaths),
y = Deaths, fill = State)) +
geom_bar(stat = "identity",show.legend = FALSE) +
labs(title = "Number of Deaths in 2021",
x = "State", y = "Number of Deaths") +
coord_flip()
weapon_violence %>%
ggplot( aes(x = reorder(State,`Number_of_ Registered_Weapon` ),
y = `Number_of_ Registered_Weapon`, fill = State)) +
geom_bar(stat = "identity",show.legend = FALSE) +
labs(title = "Number of Registered Weapon in 2021",
x = "State", y = "Number of Registered Weapon") +
coord_flip()
weapon_violence %>%
ggplot(aes(x = `Number_of_ Registered_Weapon`, y = Deaths, col = State)) +
geom_point(size = 3, alpha = 0.7) +
geom_smooth(method = "lm", se = FALSE, col = "skyblue3", fullrange = TRUE,alpha=0.5) +
labs(title = "Scatterplot of Weapon vs Deaths",
x = "Number of Registered Weapon",
y = "Number of Deaths")
## `geom_smooth()` using formula = 'y ~ x'
